ClawGuard detects LLM agent workflow hijacking by capturing and classifying electromagnetic emanations from hardware with 0.9945 AUC, 100% true-positive rate, and 1.16% false-positive rate on a 7.82 TB RF dataset.
SAGA: Governing AI agent security
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
years
2026 7representative citing papers
GAAP guarantees confidentiality of private user data for AI agents by enforcing user-specified permissions deterministically through persistent information flow tracking, without trusting the agent or requiring attack-free models.
MolTrust deploys a W3C VC+DID trust infrastructure for AI agents with kernel-layer authorization, cross-protocol interoperability, and layered Sybil resistance, operational since March 2026 across eight verticals.
The EU AI Act narrows accountability for multi-agent AI in critical infrastructure by excluding safety components from key explanation and impact assessment rights, and the paper proposes AgentGov-SC, a three-layer architecture with 25 measures to address this through traceability to existing AI and
Bounded autonomy using typed action contracts and consumer-side execution lets LLMs safely operate enterprise systems, achieving 23 of 25 tasks with zero unsafe executions versus 17 for unconstrained AI across 25 trials.
The paper systematizes security for LLM agents in agentic commerce into five threat dimensions, identifies 12 cross-layer attack vectors, and proposes a layered defense architecture.
Security practitioners use LLMs independently for low-risk productivity tasks while showing interest in enterprise platforms, but reliability, verification needs, and security risks limit broader autonomy.
citing papers explorer
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ClawGuard: Out-of-Band Detection of LLM Agent Workflow Hijacking via EM Side Channel
ClawGuard detects LLM agent workflow hijacking by capturing and classifying electromagnetic emanations from hardware with 0.9945 AUC, 100% true-positive rate, and 1.16% false-positive rate on a 7.82 TB RF dataset.
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An AI Agent Execution Environment to Safeguard User Data
GAAP guarantees confidentiality of private user data for AI agents by enforcing user-specified permissions deterministically through persistent information flow tracking, without trusting the agent or requiring attack-free models.
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From Specification to Deployment: Empirical Evidence from a W3C VC + DID Trust Infrastructure for Autonomous Agents
MolTrust deploys a W3C VC+DID trust infrastructure for AI agents with kernel-layer authorization, cross-protocol interoperability, and layered Sybil resistance, operational since March 2026 across eight verticals.
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Governing What the EU AI Act Excludes: Accountability for Autonomous AI Agents in Smart City Critical Infrastructure
The EU AI Act narrows accountability for multi-agent AI in critical infrastructure by excluding safety components from key explanation and impact assessment rights, and the paper proposes AgentGov-SC, a three-layer architecture with 25 measures to address this through traceability to existing AI and
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Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution
Bounded autonomy using typed action contracts and consumer-side execution lets LLMs safely operate enterprise systems, achieving 23 of 25 tasks with zero unsafe executions versus 17 for unconstrained AI across 25 trials.
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SoK: Security of Autonomous LLM Agents in Agentic Commerce
The paper systematizes security for LLM agents in agentic commerce into five threat dimensions, identifies 12 cross-layer attack vectors, and proposes a layered defense architecture.
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Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit
Security practitioners use LLMs independently for low-risk productivity tasks while showing interest in enterprise platforms, but reliability, verification needs, and security risks limit broader autonomy.